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Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution

Daniel W.A. Noble, Shinichi Nakagawa
doi: https://doi.org/10.1101/247064
Daniel W.A. Noble
1Ecology and Evolution Research Centre, School of Biological, Earth and Environmental Sciences, The University of New South Wales, Kensington NSW 2052, Sydney
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  • For correspondence: daniel.wa.noble@gmail.com s.nakagawa@unsw.edu.au
Shinichi Nakagawa
1Ecology and Evolution Research Centre, School of Biological, Earth and Environmental Sciences, The University of New South Wales, Kensington NSW 2052, Sydney
2Diabetes and Metabolism Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, NSW 2010, Australia
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  • For correspondence: daniel.wa.noble@gmail.com s.nakagawa@unsw.edu.au
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Abstract

  1. Ecological and evolutionary research questions are increasingly requiring the integration of research fields along with larger datasets to address fundamental local and global scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns.

  2. Planned missing data design (PMDD), where data are randomly and deliberately missed during data collection, is a simple and effective strategy to working under greater research constraints while ensuring experiments have sufficient power to address fundamental research questions. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and evaluating how data imputation procedures work under PMDD situations.

  3. Using realistic examples and simulations of multilevel data we show how a variety of research questions and data types, common in ecology and evolution, can be aided by utilizing a PMDD and data imputation procedures. More specifically, we show how PMDD can improve statistical power in detecting effects of interest even with high levels (50%) of missing data and moderate sample sizes. We also provide examples of how PMDD can facilitate improved animal welfare all the while reducing research costs and constraints that would make endeavours for integrative research challenging.

  4. Planned missing data designs are still in their infancy and we discuss some of the difficulties in their implementation and provide tentative solutions. Nonetheless, data imputation procedures are becoming more sophisticated and more easily implemented and it is likely that PMDD will be an effective and powerful tool for a wide range of experimental designs, data types and problems common in ecology and evolution.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 11, 2018.
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Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution
Daniel W.A. Noble, Shinichi Nakagawa
bioRxiv 247064; doi: https://doi.org/10.1101/247064
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Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution
Daniel W.A. Noble, Shinichi Nakagawa
bioRxiv 247064; doi: https://doi.org/10.1101/247064

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